Work

Comparison and Optimization of Generalized Stamping Machine Fault Diagnosis Models Using Various Transfer Learning Methodologies

Year: 2025

Type: article

Abstract: The integration of artificial intelligence (AI) with stamping technology has become increasingly critical in smart manufacturing, driven by advancements in both fields. Total clearance, a crucial determinant of both process and product quality in stamping operations, significantly impacts cutting precision, material deformation, and the longevity of stamping equipment. Consequently, real-time monitoring and prediction of total clearance are essential for effective process control and fault diagnosis. However, the heterogeneity of stamping machine designs necessitates the development of numerous machine-specific models, posing a significant challenge for practical implementation. This research addresses this challenge by developing a generalized fault diagnosis model applicable across multiple stamping machine types. Specifically, the model is designed to monitor four distinct machine models: OCP-110, G2-110, G2-160, and ST1-110. Vibration data, acquired using accelerometers strategically placed at two distinct sensor locations on each machine, serve as the primary input for the model. Four prominent deep learning architectures-a 10-layer convolutional neural network (CNN), a CNN with residual connections (CNN-Res), VGG16, and ResNet50-were rigorously evaluated in conjunction with fine-tuning strategies to determine the optimal model architecture. The resulting generalized fault diagnosis model achieved an average accuracy, recall rate, and F1 score exceeding 99%, demonstrating its efficacy and reliability for real-world applications. This proposed approach offers the potential for scalability to additional stamping machine types and operational conditions, thereby streamlining the deployment of predictive maintenance systems by equipment manufacturers.

Source: Sensors

DOI: 10.3390/s25061779

Authors: Po-Wen Hwang, Yuan-Jen Chang, Hsieh-Chih Tsai, Yu-Ta Tu, Hung-Pin Yang

Institution: Feng Chia University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137